4 citations · 7 across the 11 of their papers we have counts for
11 papers
Creating Impactful Autonomous Driving Datasets: A Strategic Guide from Research Gap to Benchmark
Richard Schwarzkopf, Jonas Merkert, Frank Bieder +22
Well-designed autonomous driving datasets have fundamentally shaped research progress, yet existing literature primarily describes what datasets contain rather than how to strategi…
The Road Ahead in Autonomous Driving: The KITScenes Multimodal Dataset
Richard Schwarzkopf, Fabian Immel, Alexander Blumberg +21
Existing autonomous driving datasets have enabled major progress, but fall short in sensor fidelity, map completeness, or geographic diversity. We present KITScenes Multimodal, a E…
Mosaic: An Extensible Framework for Composing Rule-Based and Learned Motion Planners
Nick Le Large, Marlon Steiner, Lingguang Wang +4
Safe and explainable motion planning remains a central challenge in autonomous driving. While rule-based planners offer predictable and explainable behavior, they often fail to gra…
Reasoning models do not yet follow their reasoning in autonomous driving: The KITScenes LongTail Dataset
Royden Wagner, Omer Sahin Tas, Jaime Villa +20
Handling rare events is the central open challenge in autonomous driving. Reasoning models, which generate explicit chains of reasoning before acting, promise to generalize to such…
Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Driving
Johannes Fischer, Marlon Steiner, Ömer Sahin Tas +1
Model predictive control (MPC) is widely used for motion planning, particularly in autonomous driving. Real-time capability of the planner requires utilizing convex approximation o…
Prediction-Driven Motion Planning: Route Integration Strategies in Attention-Based Prediction Models
Marlon Steiner, Royden Wagner, Ömer Sahin Tas +1
Combining motion prediction and motion planning offers a promising framework for enhancing interactions between automated vehicles and other traffic participants. However, this int…
Closing the Navigation Compliance Gap in End-to-end Autonomous Driving
Hanfeng Wu, Marlon Steiner, Michael Schmidt +2
Trajectory-scoring planners achieve high navigation compliance when following the expert's original command, yet they struggle at intersections when presented with alternative comm…
FlowDrive: moderated flow matching with data balancing for trajectory planning
Lingguang Wang, Ömer Şahin Taş, Marlon Steiner +1
Learning-based planners are sensitive to the long-tailed distribution of driving data. Common maneuvers dominate datasets, while dangerous or rare scenarios are sparse. This imbala…
RetroMotion: Retrocausal Motion Forecasting Models are Instructable
Royden Wagner, Omer Sahin Tas, Felix Hauser +7
Motion forecasts of road users (i.e., agents) vary in complexity depending on the number of agents, scene constraints, and interactions. In particular, the output space of joint tr…
SceneMotion: From Agent-Centric Embeddings to Scene-Wide Forecasts
Royden Wagner, Ömer Sahin Tas, Marlon Steiner +5
Self-driving vehicles rely on multimodal motion forecasts to effectively interact with their environment and plan safe maneuvers. We introduce SceneMotion, an attention-based model…
MAP-Former: Multi-Agent-Pair Gaussian Joint Prediction
Marlon Steiner, Marvin Klemp, Christoph Stiller
There is a gap in risk assessment of trajectories between the trajectory information coming from a traffic motion prediction module and what is actually needed. Closing this gap ne…